Portrait photo of Saurav Jha, IVADO postdoctoral researcher at MILA

I am an ivado postdoctoral researcher at the Chandar Research Lab in MILA, Montreal. My current research interests span multimodal generative models, agentic frameworks, and continual learning.

I completed my PhD at UNSW Sydney in August 2025, where I was advised by Lina Yao and Dong Gong. During the latter half of my PhD, I worked as an applied research scientist at openstream.ai, and as a research intern at Sony (hosted by Shiqi Yang and Shusuke Takahashi ) and Tencent (hosted by Shengju Qian).

Prior to my PhD, I completed my Erasmus Mundus Joint Master's Degree (EMJMD) in Advanced Systems Dependability from the University of St Andrews, the UK and l'Université de Lorraine, France. During my master's, I interned with the Multispeech group at Inria Nancy. I once wrote this medium blog documenting my EMJMD experience to help guide future aspirants.

[I am actively helping candidates applying for the IVADO 2026 postdoctoral fellowship. To receive concrete feedback, please mention your supervisor and your research interests/publications in your inquiry.]

news

  • Oct 2025: Quoted in this MIT news story for perspectives on personalized object localization.
  • Aug 2025: My PhD thesis was recommended for the Dean's Award for outstanding PhD theses at UNSW Sydney.
  • Jun 2025: Presented my paper "Mining Your Own Secrets" at the Sydney AI meetup .
  • May 2025: Mentoring session + Talk on continual learning in Bishesh Khanal's group, NAAMII, Nepal.
  • Apr 2025: Gave a virtual talk on continual learning in Marinka Zitnik's group, Harvard University.
  • Apr 2025: Attending ICLR'25 in Singapore to present “Mining your Own Secrets” paper.
  • Dec 2024: Presented my paper "CLAP4CLIP" at NeurIPS 2024 in Vancouver.
  • Nov 2024: Won the Tiktok-sponsored Best Student Presentation award for "CLAP4CLIP" at 2024 Sydney AI meetup.
  • Jul 2024: Presented my paper "CLAP4CLIP" at the 2nd Bayes duality workshop, RIKEN AIP, Tokyo.
  • Apr 2024: Won a travel grant for presenting my paper "NPCL" at the EEML summer school, Novi Sad, Serbia.

Experience

IVADO postdoctoral fellow (Sep 2025 - Present)

MILA • Montréal, Canada 🇨🇦

Research aligned with advancing Canada's R3AI initiative.

Applied AI Scientist (May 2025 - Aug 2025)

OpenStream.ai • Melbourne, Australia 🇦🇺

Infra-focus: Developed production-grade conversational LLM agents for enterprise clients.

ML-focus: Implemented & shipped a POC for neuro-symbolic verification of multi-agent systems.

AI Research Intern (Sep 2024 - Mar 2025)

LightSpeed Studios, Tencent • Sydney, Australia 🇦🇺

Worked on controllable image generation and preference optimization for multi-modal LLMs.

Research Scientist Intern (May 2024 - Aug 2024)

Creative AI Lab, Sony Group Corporation • Tokyo, Japan 🇯🇵

Worked on continual personalization of pre-trained text-to-image diffusion models.

Research Assistant (Sep 2021 - Jan 2022)

Computer Vision Centre, Universitat Autònoma de Barcelona • Barcelona, Spain 🇪🇸

Worked on rehearsal-free continual learning for Vision Transformers (ViTs).

Research Intern (Mar 2021 - Jul 2021)

Multispeech group, Inria Nancy • Nancy, France 🇫🇷

Worked on learning domain-specific language models for speech recognition.

Machine Learning Engineer (Jun 2018 - Jul 2019)

FactSet Research Systems Inc. • Hyderabad, India 🇮🇳

Worked on improving FactSet's named entity recognition service with acronym disambiguation and neural topic modeling.

Awards & Recognition

Academic Services

tutoring at UNSW

Selected Publications

Mining Your Own Secrets

Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models

Saurav Jha, Shiqi Yang, Masaki Ishii, Meng Zhao, Christian Simon, Jehanzeb Mirza, Dong Gong, Lina Yao, Shusuke Takahashi, Yuki Mitsufuji

ICLR 2025

Paper Project page

We propose using diffusion classifier scores for regularizing the parameter-space and function-space of text-to-image diffusion models, to achieve continual personalization.

CLAP4CLIP

CLAP4CLIP: Continual LeArning with Probabilistic finetuning for Vision-Language Models

Saurav Jha, Dong Gong, Lina Yao

NeurIPS 2024

Paper Code

Our work proposes Continual LeArning with Probabilistic finetuning (CLAP) - a probabilistic modeling frame- work over visual-guided text features per task, thus providing more calibrated CL finetuning.

NPCL

NPCL: Neural Processes for Uncertainty-Aware Continual Learning

Saurav Jha, Dong Gong, He Zhao, Lina Yao

NeurIPS 2023

Paper Code

We propose a neural process-based continual learning approach with task-specific modules arranged in a hierarchical latent variable model. We tailor regularizers on the learned latent distributions to alleviate forgetting.

Towards Exemplar-Free Continual Learning

Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization

Francesco Pelosin*, Saurav Jha*, Andrea Torsello, Bogdan Raducanu, Joost van de Weijer

CVPR 2022 Workshop on Continual Learning (CLVision)

Paper Code

We investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism.